In this paper we present a system that tracks customers in a store and performs a number of activity analysis tasks based on the output from the tracker. We obtain the trajectories by employing a human body tracking system designed as a Bayesian jump-diffusion filter. The customer travel trajectories on the floor map are extracted and post processed to remove noise. The shoppers that belong to the same group are identified by clustering their trajectories. The clustering is based on a distance metric that incorporates both time and location information. Our system also identifies shopper groups based on the proximity metric also presented in this paper. Further, store employees are detected as a separate group, based on a 2D color histogram analysis. Finally, dwelling customers, i.e the customers stopping to browse for products are detected by analyzing the behavior of the recorded trajectories.